Disruptive technologies—innovative products or processes that supplant existing technology—are commonly associated with leading-edge product development and new business models. Consider the disruptive influence mobile phones have had on the landline phone, personal computer, and music/entertainment industries. But disruptive technologies are not just of interest to consumers; they're also relevant for supply chain managers. That's because supply chain organizations can create profitable opportunities by utilizing disruptive technologies as primary drivers of innovative supply chain strategies.
There's danger, however, in adopting disruptive technologies simply because they're viewed as "the wave of the future," or because organizations worry that they'll be left behind if they don't follow the crowd. We argue that instead, it is imperative for supply chain managers to view and implement disruptive technologies strategically, by first considering customer needs, and then aligning appropriate technologies with each supply chain application, avoiding a one-size-fits-all approach. Doing so, we believe, will lead to improvements in agility, customer service, and cost—and allow you to be the disruptor, rather than the disrupted. In this article, we offer a framework for evaluating disruptive technologies from a strategic perspective.
Data as a driver of disruptive technologies
Typically, a new, disruptive technology initially costs more and does less than existing technologies; as a result, it often is actively ignored as a threat by rivals. But in many cases, the disruptive technology soon evolves into a high-quality/low-cost offering that crowds out traditional technology. The impact of disruptive technologies is often felt most clearly by the incumbent firms that are being crowded out by them.1 Examples include Kmart being elbowed out of the discount retail market by Wal-Mart's innovative distribution practices and technologies in the 1980s and 1990s, or Dell's desktop computers being supplanted by new processor, battery, and display technologies that accelerated the rise of laptops and tablets offered by Samsung, Acer, and Apple in the 2010s. Today data-driven technologies are emerging as a game-changing disruption that is already transforming and will continue to transform supply chains.
The overarching contextual theme that supply chain managers can use to turn disruptive technology into a strategic tool is data. Some of these data are coming from radically different sources than just a few years ago. How is the next wave of data-saturated supply chains impacting the already challenging jobs of supply chain managers? We will focus on three elements that directly impact supply chains:
At first glance, these rapidly changing areas may appear marginally related to the main activities of supply chain managers. However, when they are viewed through the lens of disruptive technologies, closer consideration suggests disruptive forces that will directly impact supply chains are already in motion. Thus, all supply chain managers should consider these elements and, depending on the organizational and competitive context in which they operate, take some degree of action in the near future.
1. Where we get data. There are many sources of business data. For example, data generated by enterprise resource planning (ERP) systems, including operational, financial, and human resources information, flow into most organizations. Many companies use business intelligence (BI) tools to parse and transform data into a format that assists decision making. However, there are several other data-related trends that bear consideration. For example, some companies are:
A swiftly growing source of data is the Internet of Things (IoT). The IoT consists of numerous Internet-connected sensors and switches that collect, send, and receive data that can be used to monitor and control devices and equipment, as well as predict events with minimal human intervention. In the logistics arena, IoT is a placeholder term that describes the process of taking all the disparate systems and equipment in, for example, a distribution center (conveyors, robots, automated storage and retrieval systems, automated guided vehicles, forklifts, lighting and heating, ventilation, and air conditioning systems) and tightly coupling them to warehouse-control and labor, transportation, order, and customer management systems. Such a connected warehouse would allow supply chain and warehouse managers to reach new levels of operational efficiency and predictability while providing real-time visibility into operations.
These sources are disruptively changing what we know, how we know it, and what we can do with our knowledge. Yet data by itself is not disruptive. Instead, relevant, clean, timely pieces of data, organized for analysis, are the foundation upon which disruptive technologies are built. The next step in turning disruptive technologies into a strategic advantage is to understand changes in data analysis, namely data analytics and artificial intelligence.
2. How we analyze data. We will explore two ways data are analyzed. One—data analytics (DA)—is human-driven, and the other—artificial intelligence (AI)—is human-designed but utilizes technology to learn and adapt. Both DA and AI involve the application of mathematical techniques to large data sets to identify patterns and relationships that may previously have been unnoticed. For example, if a shipper has data on everything a truck was doing before it was involved in an incident that resulted in damaged, overheated, overcooled, or missing goods, it can better determine what led to the incident and use that information to reduce future losses.2 The analysis and use of a combination of data sources—on the vehicle, in the container, on the package, and even biometrics from the vehicle's operator—can be fed into a predictive model that can then be used to drive change in the supply chain. AI could automatically change routes or shipping methods, for example, or DA could drive changes in packaging or in vendor training.3 These type of actions can lead to both cost savings and the development of premium-priced services.
Data analytics is used to make predictions with reduced errors about future events, such as when a product will be purchased, a machine will break down, or a valued employee will quit, to name a few examples. DA often uses accessible, easy-to-understand reporting mechanisms, such as "heat mapping" (a graphical representation of data where the individual values contained in a matrix are represented as colors) and multidimensional plotting, to communicate findings.
One relevant goal of data analytics is improved supply chain visibility, which results in reduced risk and shorter lead times as well as the ability to quickly identify shortages and detect quality problems at the source. This increased visibility has both positive and negative implications. On the positive side, companies use real-time visibility to monitor supply chain operations, especially for high-value assets such as pharmaceuticals that have crucial delivery timing and are subject to government-imposed regulatory and compliance requirements. With real-time visibility, organizations have near 100-percent knowledge accuracy for assets stored in and across containers, pallets, and shipping crates in their supply chains. Real-time visibility reduces personnel costs, improves response times, and decreases asset spoilage. On the negative side, when processes go wrong, errors are made, or technology fails, everything is exposed to suppliers and customers—also in real time—and there is little chance to manage perceptions.
A second, related topic is the use of algorithms. An algorithm is a clearly structured set of rules and procedures that are applied to data in order to solve problems. An extension of a rich set of algorithms drives artificial intelligence, defined as when computer systems have the ability to mimic human cognitive activity, through sensing (visual, auditory, determining hot/cold/wet, and so forth), recognition/categorization, and decision-making activity. This is followed by auto-improvement based on feedback, enabling the artificial intelligence system to learn over time. Amazon is one of many companies making significant investments in the development of AI, with both consumer-oriented and supply chain implications.4
Data analytics and artificial intelligence are just two ways that data-driven disruptive technologies are relevant and imminent. Both take streams of data as input and apply computing power, generating highly valuable, actionable information.
The third step in the disruptive technologies narrative for supply chain managers is to understand how data are transformed into actual applications in the physical world. In other words, once we process data, what does it mean inside the warehouse, in retail stores, or on the roads and in the air around us?
3. How we transform data into real-life applications. The transformation of data into disruptive technologies represents the highly visible outcomes of new data sources and new data-analysis tools. We break this down into two broad categories: changing how we move goods, and changing how we manufacture.
Changing how we move goods. The rise of unmanned autonomous vehicles (UAVs) has long been predicted as imminent. Now we are seeing UAVs in actual use inside closed spaces like manufacturing plants and distribution centers. Advanced UAVs also are starting to move onto public roads and into the skies.
Self-driving vehicles and aerial drones use advanced sensors, satellite data, and peer-to-peer communications to move from point to point with limited or no human intervention. These capabilities could not only reduce costs but could also make it possible to provide premium services, such as delivering products or services in unserved areas, that competitors cannot offer.
On the inbound logistics side, autonomous delivery of materials and components by trucks and rail could reduce errors, increase delivery speed (no driver breaks required), and drive down costs. In the warehouse, robots are already reducing errors and cost. And on the outbound side, autonomous delivery is expected to change when and how customers can receive goods.
What does this mean for supply chain managers? Drone-based delivery may eventually set the new standard for fast delivery. Self-driving forklifts (autonomous vehicles combined with advanced robotics) could easily be the next evolution of autonomous vehicles in distribution centers. Taking human labor out of supply chain activities can save time and cost while improving accuracy. However, the risks of technology failure are exacerbated in today's lean environment. For example, a single system failure could result in the shutdown of an entire distribution center. In addition, Web-enabled tools are at risk of data-security breaches that can have similar negative effects.
Given these uncertainties, how should supply chain managers proceed in regard to implementing autonomous vehicles? We believe it is imperative that supply chain managers strategically determine customer needs and partner capabilities, and align appropriate technologies with each application where autonomous vehicles may be appropriate, avoiding an indiscriminate or one-size-fits-all approach.
Changing how we manufacture. Three-dimensional (3-D) printing involves the creation of objects using a machine that typically melts and distributes layers of a growing variety of materials, including plastics, metal, glass, and ceramics, in a pattern described in a digital file that contains the specifications of the item to be created.5 Actual examples include toys, spare parts for the International Space Station, food, and even human organs! Implications for the supply chain include:
This technology can facilitate customization and the use of more innovative and functional designs. It also offers the potential to combine process steps required in traditional manufacturing, with the added advantage of requiring no specialized tooling. It will change what we source, as we will be buying feedstock for 3-D printers rather than components. Because 3-D printing enables the manufacturing of customized products on demand, in lots as small as one, many organizations could create a decentralized network of small, fully flexible production centers that would allow them to manufacture small lots of highly customized products close to the point of demand. Such a model would reduce complexity, eliminate the need for large lot sizes and high inventory levels, and take time and cost out of transportation.7 Spreading out manufacturing locations so that production occurs closer to the end customer would also reduce lead times and improve responsiveness. In fact, 3-D printing could even help shift manufacturing away from low-cost regions and closer to the customer base.
Looking more broadly, 3-D printing will usher in new competitors with new ways of delivering products and services to customers. It disrupts the value-creation process by changing how we produce—we will come back around to crafting individual products, but now by data-driven machines rather than by human hands.
With all its obvious benefits, 3-D printing also carries some risks, most prominently that of a new age of "digital piracy" that could erode traditional, value-added supply chains. In this new era, designs can be digitally scanned and shared or sold over the Internet. This may challenge companies' ability to control their proprietary designs. It may also result in loss of revenue from "knock-off" products sold on the gray market for a fraction of the original product's price, or from customers printing their own additional or replacement products.
A contingency-based approach
The typical human response to change is to either ignore or avoid it. Supply chain managers cannot afford to go that route and must instead think strategically about disruptive technologies. We recommend a contingency-based approach. That is, determine the characteristics of your organization and the industry in which you compete, and then select a strategy and related tactics that align with your organization and its competitive context. This can allow you to invite disruptive technology into your supply chain in a way that allows you to manage it.
Contingency-based approaches to strategy, such as the one described by Hofer,8 have been around for decades. The idea is that managers should not adhere to one rigid strategy, but rather adopt an appropriate strategy depending on the situation. Managers should consider how their competitive environment is changing and create alternative strategies for the most probable scenarios. For example, they may want to conduct sensitivity analyses to understand the impact of changes in currency-exchange rates or raw-material prices on their decisions, and then identify possible strategic alternatives.
With that in mind, we offer a framework for conversations about and actions in response to disruptive technologies. For the sake of simplicity, the framework includes four questions supply chain managers can explore to better plan for and adapt to disruptive technologies. Much of this work may have already been done in your organization as part of strategic planning exercises, supplier certifications, or external audits, so you may be able to complete this analysis quickly. Figure 1 summarizes the following information in graphical form, with each answer in the listed options corresponding to a green, yellow, or red circle.
Question 1. How much havoc could disruptive technologies cause within your business model? To what extent might a disruptive technology transform your:
The greater the potential to disrupt your business model, the higher the priority and attention you should give to this issue.
Question 2. What is the competitive landscape in your industry? To what extent will a disruptive technology:
More potential competition drives prices down. In response, supply chain managers should increase attention and priority to potential disruptions.
Question 3. What is the expected velocity of change? For each of the disruptive technologies, what is the time frame for potential disruption, and how likely is each new technology to be adopted? The faster the velocity of change is expected to be, and the more likely it is that the innovation will be adopted, the greater your attention and priority should be.
It's important to consider those questions in the context of your competitive positioning. In each of your markets, what is your current competitive position—leader, middle of the pack, laggard, or up-and-coming? If you do not already have a strong market position, then the imperative for action will be greater than for those that do, because your company will not have the reputation, assets, and channel control that could buffer you from the changes.
Question 4. Will this disruptive technology give you a competitive advantage that would be difficult for rivals to imitate? Can successful adoption of disruptive technology give you an edge over your rivals that would be valuable to customers? If so, how easy would it be for rivals to imitate? For example, using off-the-shelf technology in a straightforward way will never provide a robust advantage. The more likely it is that a new technology can give you a protectable and valuable advantage, the greater your attention and priority should be.
Now, how should you respond to the resulting patterns?
Should you ignore disruptive technologies you believe do not matter to your company? It is probably best to never ignore disruptive technologies. Instead, it's advisable to at least monitor news feeds, social media, trade shows, marketing materials, and industry newsletters. Also, listen to what vendors have to say. You can categorize the status of each trend using the same green/yellow/red organizing scheme we have just discussed.
Recommendations for managers
It's critical that supply chain managers be able to accurately answer the questions posed in Figure 1. The effectiveness of the analysis, however, will inevitably be influenced by the mix of people who are engaged in that process. Having a diverse set of perspectives that can address competitive position, current level of in-house expertise, supplier capabilities, and customer requirements will yield the most valuable outcomes.
When discussed in an open environment, the output of this contingency-based planning approach will be a menu of adoption and investment options for each disruptive technology perceived as imminent and impactful. This is consistent with Coyle and Ruamsook's advice to "look out" to identify disruptive exogenous forces, "look around" to gauge the competitive climate, and "look in" to assess your resources and capabilities.9
In summary, the important takeaway for supply chain managers who are inviting disruptive technologies into their supply chains is that "one size does not fit all." There is no single formula that will work for every company or supply chain organization—except, perhaps, for this piece of advice: Don't ignore disruptive technologies!
1. Clayton M. Christensen, The Innovator's Dilemma (New York: HarperBusiness, 1997).
2. Michael Watson, "Three things you should know about big data and analytics," CSCMP's Supply Chain Quarterly, Quarter 3/2014, 44-49.
3. Andy Souders, "From Desert Storm to the retail store: Five technologies that are closing global supply chain gaps," CSCMP's Supply Chain Quarterly, Quarter 4/2015, 42-45.
4. Kevin O'Marah, "Supply Chains and Artificial Intelligence: Ask Jeff Bezos," Forbes, June 6, 2016, www.forbes.com.
5. AndrÃ© Kieviet and Suraj Alexander, "Is Your Supply Chain Ready for Additive Manufacturing?" Supply Chain Management Review, May/June 2015, 34-39.
6. Richard D'Aveni, "The 3-D Printing Revolution," Harvard Business Review, May 2015, 40-48.
7. Kieviet and Alexander, "Is Your Supply Chain Ready for Additive Manufacturing?"
8. C.W. Hofer, "Toward a contingency theory of business strategy," Academy of Management Journal 18 (1975): 784-810.
9. John J. Coyle and Kusumal Ruamsook, "T = MIC2: Game-changing trends and supply chain's 'new normal,' " CSCMP's Supply Chain Quarterly, Quarter 4/2014, 22-29.